Functional Magnetic Resonance Imaging: Data Acquisition and Analysis HST 583

This course provides the background necessary for understanding functional MRI and hands-on instruction in designing and conducting fMRI studies of the human brain, including data acquisition, analysis, and interpretation.

The course provides thorough introductions to the physics of MR signal generation and image encoding, the mechanisms of anatomical and functional contrasts, the physiological basis of the fMRI signals, cerebral hemodynamics, and neurovascular coupling. Basic neuroanatomy, vascular anatomy, and physiology are reviewed.

Design methods for stimulus-, task-driven and resting-state fMRI experiments will be taught; workflows for model-based and data-driven analysis methods for fMRI data are taught. Brain structure analysis and automatic segmentation and registration methods for surface- and region-based analyses of fMRI data are taught. Analysis methods covered include MVPA, ICA, graph theory, computational modeling and non-parametric statistics.

Guided exploration of special advanced topics including accelerated parallel imaging and Simultaneous Multi-Slice (MultiBand) imaging, ultra-high spatial and temporal resolution imaging, new insights into neurovascular coupling, integrating functional MRI with PET, diffusion MRI data, connectomics, large-scale neuroimaging initiatives, Big Data analysis and machine learning, are offered in discussion sections.

Human subject issues including informed consent, institutional review board requirements and safety in environments with high magnetic field strengths are presented.

Two lectures per week, one laboratory session per week, and one advanced-topics discussion session per week. Laboratory sessions include fMRI data acquisition sessions at the 3 Tesla MRI scanner at MIT and the Connectom and 7 Tesla scanners at the Martinos Center @ MGH, Charlestown, as well as hands-on data analysis workshops.

Assignments include readings of textbook chapters and primary literature as well as fMRI data analysis in the laboratory sessions. Knowledge of basic probability & statistics and linear algebra required; introductory or college-level neurobiology, physics, and signal processing are helpful.

Course Directors: Susan Whitfield-Gabrieli and Jonathan Polimeni

Laboratory Director: Anastasia Yendiki

Instructors: R. L. Gollub, L. Wald, B. Dickerson, S. Whitfield-Gabrieli, D. Bolar, D. Salat, M. Vangel, D. Greve, S. Pujol, D. McLaren, L. Nickerson, J. Augustinack, K. Setsompop, J. Cohen-Adad, S. Ghosh, A. Takahashi, B. Rosen, I. Aganj, K. Setsompop, M. Loggia, P. Kumar, A. Yendiki.

Offered alternate years: Fall

For more information, contact Sue Whitfield-Gabrieli, Ph.D. or Jon Polimeni, Ph.D., visit the HST583 Course Website, download the course flyer or syllabus.